clspec: Lspec: logspline estimation of a spectral distribution

Description

Autocorrelations, autocovariances (clspec), spectral densities and line spectrum (dlspec), spectral distributions (plspec) or a random time series(rlspec) from a model fitted with lspec.

Usage

clspec(lag, fit, cov = TRUE, mm) 
dlspec(freq, fit) 
plspec(freq, fit, mm) 
rlspec(n, fit, mean = 0, cosmodel = FALSE, mm)

Arguments

lag

vector of integer-valued lags for which the autocorrelations or autocorrelations are to be computed.

fit

lspec object, typically the result of lspec.

cov

compute autocovariances (TRUE) or autocorrelations (FALSE).

number of points used in integration and the fft. Default is the smallest power of two larger than max(fit$sample, max(lag),1024) for clspec and plspec or the smallest power of two larger than max(fit$sample,

freq

vector of frequencies. For plspec frequencies should be between $-\pi$ and $\pi$.

length of the random time series to be generated.

mean

mean level of the time series to be generated.

cosmodel

indicate that the data should be generated from a model with constant harmonic terms rather than a true Gaussian time series.

Value

Autocovariances or autocorrelations (clspec); values of the spectral distribution at the requested frequencies. (plspec); random time series of length n (code{rlspec}); or a list with three components (code{dlspec}):
dthe spectral density evaluated at the vector of frequencies,
modfreqmodified frequencies of the form $\frac{2\pi j}{T}$ that are close to the frequencies that were requested,
mmass of the line spectrum at the modified frequencies.

References

Charles Kooperberg, Charles J. Stone, and Young K. Truong (1995). Logspline Estimation of a Possibly Mixed Spectral Distribution. Journal of Time Series Analysis, 16, 359-388.

Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K. Truong. The use of polynomial splines and their tensor products in extended linear modeling (with discussion) (1997). Annals of Statistics, 25, 1371--1470.

Examples

Run this code

data(co2)
co2.detrend <- lm(co2~c(1:length(co2)))$residuals
fit <- lspec(co2.detrend)
clspec(0:12,fit)
plspec((0:314)/100, fit)
dlspec((0:314)/100, fit)
rlspec(length(co2),fit)

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